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Опубликовано 2 декабря 2020, 20:11
Bayesian User Modeling for Inferring the Goals and Needs of Software Users
In this 1995 video from Microsoft Research, Eric Horvitz demonstrates the Lumiere system. The Lumiere Project was an early exploration of using probability and expected utility to provide assistance to computer software users. The project centered on performing inferences about a computer user's needs by considering a user's background, actions, and queries. Several problems were tackled in Lumiere research, including (1) the construction of Bayesian models for reasoning about the time-varying goals of computer users from their observed actions and queries, (2) gaining access to a stream of events from software applications, (3) developing a language for transforming system events into observational variables represented in Bayesian user models, (4) developing persistent profiles to capture changes in a user's expertise, and (5) the development of an overall architecture for an intelligent user interface.
In Part I of the video Eric demonstrates the main system and its functionality. In Part II, he discusses the feasibility of driving non-traditional, “social user interfaces” with Lumiere’s inferences. Lumiere prototypes served as the basis for components of the Office Assistant in Microsoft Office, first shipping to the public in Office '97.
Learn more about this demonstration of Lumiere at microsoft.com/en-us/research/v...
In this 1995 video from Microsoft Research, Eric Horvitz demonstrates the Lumiere system. The Lumiere Project was an early exploration of using probability and expected utility to provide assistance to computer software users. The project centered on performing inferences about a computer user's needs by considering a user's background, actions, and queries. Several problems were tackled in Lumiere research, including (1) the construction of Bayesian models for reasoning about the time-varying goals of computer users from their observed actions and queries, (2) gaining access to a stream of events from software applications, (3) developing a language for transforming system events into observational variables represented in Bayesian user models, (4) developing persistent profiles to capture changes in a user's expertise, and (5) the development of an overall architecture for an intelligent user interface.
In Part I of the video Eric demonstrates the main system and its functionality. In Part II, he discusses the feasibility of driving non-traditional, “social user interfaces” with Lumiere’s inferences. Lumiere prototypes served as the basis for components of the Office Assistant in Microsoft Office, first shipping to the public in Office '97.
Learn more about this demonstration of Lumiere at microsoft.com/en-us/research/v...
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